OpenRaiser/Intern-Atlas huggingface dataset:
Positioning AI Tools to Support Online Harm Reduction Practice: Applications and Design Directions
Venue: arXiv cs.HC 2025
Year: 2025

Access to accurate and actionable harm reduction information can directly impact the health outcomes of People Who Use Drugs (PWUD), yet existing online channels often fail to meet their diverse and dynamic needs due to limitations in adaptability, accessibility, and the pervasive impact of stigma. Large Language Models (LLMs) present a novel opportunity to enhance information provision, but their application in such a high-stakes domain is under-explored and presents socio-technical challenges. This paper investigates how LLMs can be responsibly designed to support the information needs of PWUD. Through a qualitative workshop involving diverse stakeholder groups (academics, harm reduction practitioners, and an online community moderator), we explored LLM capabilities, identified potential use cases, and delineated core design considerations. Our findings reveal that while LLMs can address some existing information barriers (e.g., by offering responsive, multilingual, and potentially less stigmatising interactions), their effectiveness is contingent upon overcoming challenges related to ethical alignment with harm reduction principles, nuanced contextual understanding, effective communication, and clearly defined operational boundaries. We articulate design pathways emphasising collaborative co-design with experts and PWUD to develop LLM systems that are helpful, safe, and responsibly governed. This work contributes empirically grounded insights and actionable design considerations for the responsible development of LLMs as supportive tools within the harm reduction ecosystem.
SparStencil: Retargeting Sparse Tensor Cores to Scientific Stencil Computations via Structured Sparsity Transformation
Venue: arXiv cs.CE 2025
Year: 2025

Sparse Tensor Cores offer exceptional performance gains for AI workloads by exploiting structured 2:4 sparsity. However, their potential remains untapped for core scientific workloads such as stencil computations, which exhibit irregular sparsity patterns.This paper presents SparStencil, the first system to retarget sparse TCUs for scientific stencil computations through structured sparsity transformation. SparStencil introduces three key techniques: (1) Adaptive Layout Morphing, which restructures stencil patterns into staircase-aligned sparse matrices via a flatten-and-crush pipeline; (2) Structured Sparsity Conversion, which formulates transformation as a graph matching problem to ensure compatibility with 2:4 sparsity constraints; (3) Automatic Kernel Generation, which compiles transformed stencils into optimized sparse MMA kernels via layout search and table-driven memory mapping. Evaluated on 79 stencil kernels spanning diverse scientific domains, SparStencil achieves up to 7.1x speedup (3.1x on average) over state-of-the-art framework while reducing code complexity and matching or exceeding expert-tuned performance in both compute throughput and memory efficiency.
MusiXQA: Advancing Visual Music Understanding in Multimodal Large Language Models
Venue: arXiv cs.CV 2025
Year: 2025

Multimodal Large Language Models (MLLMs) have achieved remarkable visual reasoning abilities in natural images, text-rich documents, and graphic designs. However, their ability to interpret music sheets remains underexplored. To bridge this gap, we introduce MusiXQA, the first comprehensive dataset for evaluating and advancing MLLMs in music sheet understanding. MusiXQA features high-quality synthetic music sheets generated via MusiXTeX, with structured annotations covering note pitch and duration, chords, clefs, key/time signatures, and text, enabling diverse visual QA tasks. Through extensive evaluations, we reveal significant limitations of current state-of-the-art MLLMs in this domain. Beyond benchmarking, we developed Phi-3-MusiX, an MLLM fine-tuned on our dataset, achieving significant performance gains over GPT-based methods. The proposed dataset and model establish a foundation for future advances in MLLMs for music sheet understanding. Code, data, and model will be released upon acceptance.
VisionScores -- A system-segmented image score dataset for deep learning tasks
Venue: arXiv cs.CV 2025
Year: 2025

VisionScores presents a novel proposal being the first system-segmented image score dataset, aiming to offer structure-rich, high information-density images for machine and deep learning tasks. Delimited to two-handed piano pieces, it was built to consider not only certain graphic similarity but also composition patterns, as this creative process is highly instrument-dependent. It provides two scenarios in relation to composer and composition type. The first, formed by 14k samples, considers works from different authors but the same composition type, specifically, Sonatinas. The latter, consisting of 10.8K samples, presents the opposite case, various composition types from the same author, being the one selected Franz Liszt. All of the 24.8k samples are formatted as grayscale jpg images of $128 \times 512$ pixels. VisionScores supplies the users not only the formatted samples but the systems' order and pieces' metadata. Moreover, unsegmented full-page scores and the pre-formatted images are included for further analysis.
Flexible Intelligent Metasurface for Enhancing Multi-Target Wireless Sensing
Venue: arXiv cs.IT 2025
Year: 2025

Flexible intelligent metasurface (FIM) has emerged as a transformative technology to enhance wireless sensing by dynamically morphing its three-dimensional (3D) surface shape and electromagnetic response. Unlike conventional rigid arrays, an FIM consists of low-cost radiating elements that can independently adjust their positions and radiation characteristics, thereby allowing for real-time optimization of the sensing environment. This paper investigates the impact of FIM on wireless sensing performance. Specifically, we focus on the maximization of the cumulated power of the probing signals at the target locations under the per-antenna power constraint by jointly optimizing the transmit covariance matrix and the surface shape of the transmitting FIM. We propose a block coordinate descend (BCD) algorithm to find a locally optimal solution, by alternatively updating the FIM surface shape and the transmit covariance matrix, while keeping the other one fixed at each step. Furthermore, we analyze the computational complexity and convergence properties of the proposed algorithm and demonstrate that FIM enhances wireless sensing by providing a new design degree-of-freedom to coordinate the correlation between steering vectors at different angles. Numerical results demonstrate that FIM significantly improves wireless sensing performance under the considered multi-target scenario.
Boosting LLM's Molecular Structure Elucidation with Knowledge Enhanced Tree Search Reasoning
Venue: arXiv cs.CL 2025
Year: 2025

Molecular structure elucidation involves deducing a molecule's structure from various types of spectral data, which is crucial in chemical experimental analysis. While large language models (LLMs) have shown remarkable proficiency in analyzing and reasoning through complex tasks, they still encounter substantial challenges in molecular structure elucidation. We identify that these challenges largely stem from LLMs' limited grasp of specialized chemical knowledge. In this work, we introduce a Knowledge-enhanced reasoning framework for Molecular Structure Elucidation (K-MSE), leveraging Monte Carlo Tree Search for test-time scaling as a plugin. Specifically, we construct an external molecular substructure knowledge base to extend the LLMs' coverage of the chemical structure space. Furthermore, we design a specialized molecule-spectrum scorer to act as a reward model for the reasoning process, addressing the issue of inaccurate solution evaluation in LLMs. Experimental results show that our approach significantly boosts performance, particularly gaining more than 20% improvement on both GPT-4o-mini and GPT-4o. Our code is available at https://github.com/HICAI-ZJU/K-MSE.
Text2VectorSQL: Towards a Unified Interface for Vector Search and SQL Queries
Venue: arXiv cs.CL 2025
Year: 2025

The proliferation of unstructured data poses a fundamental challenge to traditional database interfaces. While Text-to-SQL has democratized access to structured data, it remains incapable of interpreting semantic or multi-modal queries. Concurrently, vector search has emerged as the de facto standard for querying unstructured data, but its integration with SQL-termed VectorSQL-still relies on manual query crafting and lacks standardized evaluation methodologies, creating a significant gap between its potential and practical application. To bridge this fundamental gap, we introduce and formalize Text2VectorSQL, a novel task to establish a unified natural language interface for seamlessly querying both structured and unstructured data. To catalyze research in this new domain, we present a comprehensive foundational ecosystem, including: (1) A scalable and robust pipeline for synthesizing high-quality Text-to-VectorSQL training data. (2) VectorSQLBench, the first large-scale, multi-faceted benchmark for this task, encompassing 12 distinct combinations across three database backends (SQLite, PostgreSQL, ClickHouse) and four data sources (BIRD, Spider, arXiv, Wikipedia). (3) Several novel evaluation metrics designed for more nuanced performance analysis. Extensive experiments not only confirm strong baseline performance with our trained models, but also reveal the recall degradation challenge: the integration of SQL filters with vector search can lead to more pronounced result omissions than in conventional filtered vector search. By defining the core task, delivering the essential data and evaluation infrastructure, and identifying key research challenges, our work lays the essential groundwork to build the next generation of unified and intelligent data interfaces. Our repository is available at https://github.com/OpenDCAI/Text2VectorSQL.
FairI Tales: Evaluation of Fairness in Indian Contexts with a Focus on Bias and Stereotypes
Venue: arXiv cs.CL 2025
Year: 2025

Existing studies on fairness are largely Western-focused, making them inadequate for culturally diverse countries such as India. To address this gap, we introduce INDIC-BIAS, a comprehensive India-centric benchmark designed to evaluate fairness of LLMs across 85 identity groups encompassing diverse castes, religions, regions, and tribes. We first consult domain experts to curate over 1,800 socio-cultural topics spanning behaviors and situations, where biases and stereotypes are likely to emerge. Grounded in these topics, we generate and manually validate 20,000 real-world scenario templates to probe LLMs for fairness. We structure these templates into three evaluation tasks: plausibility, judgment, and generation. Our evaluation of 14 popular LLMs on these tasks reveals strong negative biases against marginalized identities, with models frequently reinforcing common stereotypes. Additionally, we find that models struggle to mitigate bias even when explicitly asked to rationalize their decision. Our evaluation provides evidence of both allocative and representational harms that current LLMs could cause towards Indian identities, calling for a more cautious usage in practical applications. We release INDIC-BIAS as an open-source benchmark to advance research on benchmarking and mitigating biases and stereotypes in the Indian context.
ParticleFormer: A 3D Point Cloud World Model for Multi-Object, Multi-Material Robotic Manipulation
Venue: arXiv cs.RO 2025
Year: 2025

3D world models (i.e., learning-based 3D dynamics models) offer a promising approach to generalizable robotic manipulation by capturing the underlying physics of environment evolution conditioned on robot actions. However, existing 3D world models are primarily limited to single-material dynamics using a particle-based Graph Neural Network model, and often require time-consuming 3D scene reconstruction to obtain 3D particle tracks for training. In this work, we present ParticleFormer, a Transformer-based point cloud world model trained with a hybrid point cloud reconstruction loss, supervising both global and local dynamics features in multi-material, multi-object robot interactions. ParticleFormer captures fine-grained multi-object interactions between rigid, deformable, and flexible materials, trained directly from real-world robot perception data without an elaborate scene reconstruction. We demonstrate the model's effectiveness both in 3D scene forecasting tasks, and in downstream manipulation tasks using a Model Predictive Control (MPC) policy. In addition, we extend existing dynamics learning benchmarks to include diverse multi-material, multi-object interaction scenarios. We validate our method on six simulation and three real-world experiments, where it consistently outperforms leading baselines by achieving superior dynamics prediction accuracy and less rollout error in downstream visuomotor tasks. Experimental videos are available at https://suninghuang19.github.io/particleformer_page/.
Flatness-based Finite-Horizon Multi-UAV Formation Trajectory Planning and Directionally Aware Collision Avoidance Tracking
Venue: arXiv cs.RO 2025
Year: 2025

Optimal collision-free formation control of the unmanned aerial vehicle (UAV) is a challenge. The state-of-the-art optimal control approaches often rely on numerical methods sensitive to initial guesses. This paper presents an innovative collision-free finite-time formation control scheme for multiple UAVs leveraging the differential flatness of the UAV dynamics, eliminating the need for numerical methods. We formulate a finite-time optimal control problem to plan a formation trajectory for feasible initial states. This optimal control problem in formation trajectory planning involves a collective performance index to meet the formation requirements to achieve relative positions and velocity consensus. It is solved by applying Pontryagin's principle. Subsequently, a collision-constrained regulating problem is addressed to ensure collision-free tracking of the planned formation trajectory. The tracking problem incorporates a directionally aware collision avoidance strategy that prioritizes avoiding UAVs in the forward path and relative approach. It assigns lower priority to those on the sides with an oblique relative approach, disregarding UAVs behind and not in the relative approach. The high-fidelity simulation results validate the effectiveness of the proposed control scheme.
Markov Chains of Evolutionary Games with a Small Number of Players
Venue: arXiv cs.GT 2025
Year: 2025

We construct and study the transition probability matrix of evolutionary games in which the number of players is finite (and relatively small) of such games. We use a simplified version of the population games studied by Sandholm. After laying out a general framework we concentrate on specific examples, involving the Iterated Prisoner's Dilemma, the Iterated Stag Hunt, and the Rock-Paper-Scissors game. Also we consider several revision protocols: Best Response, Pairwise Comparison, Pairwise Proportional Comparison etc. For each of these we explicitly construct the MC transition probability matrix and study its properties.
Forget-MI: Machine Unlearning for Forgetting Multimodal Information in Healthcare Settings
Venue: arXiv cs.LG 2025
Year: 2025

Privacy preservation in AI is crucial, especially in healthcare, where models rely on sensitive patient data. In the emerging field of machine unlearning, existing methodologies struggle to remove patient data from trained multimodal architectures, which are widely used in healthcare. We propose Forget-MI, a novel machine unlearning method for multimodal medical data, by establishing loss functions and perturbation techniques. Our approach unlearns unimodal and joint representations of the data requested to be forgotten while preserving knowledge from the remaining data and maintaining comparable performance to the original model. We evaluate our results using performance on the forget dataset, performance on the test dataset, and Membership Inference Attack (MIA), which measures the attacker's ability to distinguish the forget dataset from the training dataset. Our model outperforms the existing approaches that aim to reduce MIA and the performance on the forget dataset while keeping an equivalent performance on the test set. Specifically, our approach reduces MIA by 0.202 and decreases AUC and F1 scores on the forget set by 0.221 and 0.305, respectively. Additionally, our performance on the test set matches that of the retrained model, while allowing forgetting. Code is available at https://github.com/BioMedIA-MBZUAI/Forget-MI.git
Learning-to-Context Slope: Evaluating In-Context Learning Effectiveness Beyond Performance Illusions
Venue: arXiv cs.CL 2025
Year: 2025

In-context learning (ICL) has emerged as an effective approach to enhance the performance of large language models (LLMs). However, its effectiveness varies significantly across models and tasks, posing challenges for practitioners to determine when ICL reliably improves performance. Current evaluation approaches, reliant on performance change after applying ICL, suffer from low reliability, poor attribution, and impracticality in data-insufficient scenarios. We propose the Learning-to-Context Slope (LCS), a novel metric that quantifies ICL effectiveness by modeling the slope between learning gain (loss decrease from demonstrations) and contextual relevance (demonstration-input relevance). LCS addresses key limitations of performance-based metrics: (1) it captures continuous loss changes even when outputs are incorrect, improving reliability; (2) its formulation attributes ICL failures to weak contextual alignment (inability to adapt inputs to demonstrations) or strong output calibration (self-verification of correctness); and (3) it minimizes reliance on labeled data via synthetic evaluation. Extensive experiments demonstrate that LCS strongly correlates with performance improvements in labeled settings and reliably reflects true effectiveness in biased or data-scarce scenarios. Further analysis reveals actionable thresholds for LCS and identifies model capabilities critical to ICL success.
Community-Based Efficient Algorithms for User-Driven Competitive Influence Maximization in Social Networks
Venue: arXiv cs.SI 2025
Year: 2025

Nowadays, people in the modern world communicate with their friends, relatives, and colleagues through the internet. Persons/nodes and communication/edges among them form a network. Social media networks are a type of network where people share their views with the community. There are several models that capture human behavior, such as a reaction to the information received from friends or relatives. The two fundamental models of information diffusion widely discussed in the social networks are the Independent Cascade Model and the Linear Threshold Model. Liu et al. [1] propose a variant of the linear threshold model in their paper title User-driven competitive influence Maximization(UDCIM) in social networks. Authors try to simulate human behavior where they do not make a decision immediately after being influenced, but take a pause for a while, and then they make a final decision. They propose the heuristic algorithms and prove the approximation factor under community constraints( The seed vertices belong to an identical community). Even finding the community is itself an NP-hard problem. In this article, we extend the existing work with algorithms and LP-formation of the problem. We also implement and test the LP-formulated equations on small datasets by using the Gurobi Solver [2]. We furthermore propose one heuristic and one genetic algorithm. The extensive experimentation is carried out on medium to large datasets, and the outcomes of both algorithms are plotted in the results and discussion section.
Attribution assignment for deep-generative sequence models enables interpretability analysis using positive-only data
Venue: arXiv cs.LG 2025
Year: 2025

Generative machine learning models offer a powerful framework for therapeutic design by efficiently exploring large spaces of biological sequences enriched for desirable properties. Unlike supervised learning methods, which require both positive and negative labeled data, generative models such as LSTMs can be trained solely on positively labeled sequences, for example, high-affinity antibodies. This is particularly advantageous in biological settings where negative data are scarce, unreliable, or biologically ill-defined. However, the lack of attribution methods for generative models has hindered the ability to extract interpretable biological insights from such models. To address this gap, we developed Generative Attribution Metric Analysis (GAMA), an attribution method for autoregressive generative models based on Integrated Gradients. We assessed GAMA using synthetic datasets with known ground truths to characterize its statistical behavior and validate its ability to recover biologically relevant features. We further demonstrated the utility of GAMA by applying it to experimental antibody-antigen binding data. GAMA enables model interpretability and the validation of generative sequence design strategies without the need for negative training data.
Trident: Detecting Face Forgeries with Adversarial Triplet Learning
Venue: arXiv cs.CV 2025
Year: 2025

As face forgeries generated by deep neural networks become increasingly sophisticated, detecting face manipulations in digital media has posed a significant challenge, underscoring the importance of maintaining digital media integrity and combating visual disinformation. Current detection models, predominantly based on supervised training with domain-specific data, often falter against forgeries generated by unencountered techniques. In response to this challenge, we introduce \textit{Trident}, a face forgery detection framework that employs triplet learning with a Siamese network architecture for enhanced adaptability across diverse forgery methods. \textit{Trident} is trained on curated triplets to isolate nuanced differences of forgeries, capturing fine-grained features that distinguish pristine samples from manipulated ones while controlling for other variables. To further enhance generalizability, we incorporate domain-adversarial training with a forgery discriminator. This adversarial component guides our embedding model towards forgery-agnostic representations, improving its robustness to unseen manipulations. In addition, we prevent gradient flow from the classifier head to the embedding model, avoiding overfitting induced by artifacts peculiar to certain forgeries. Comprehensive evaluations across multiple benchmarks and ablation studies demonstrate the effectiveness of our framework. We will release our code in a GitHub repository.